Pandas DataFrame: Select rows from a given DataFrame based on values in some columns

Last update on December 21 2024 07:41:58 (UTC/GMT +8 hours)

Write a Pandas program to select rows from a given DataFrame based on values in some columns.

Sample data:
Original DataFrame
col1 col2 col3
0 1 4 7
1 4 5 8
2 3 6 9
3 4 7 0
4 5 8 1
Rows for colum1 value == 4
col1 col2 col3
1 4 5 8
3 4 7 0

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}
df = pd.DataFrame(data=d)
print("Original DataFrame")
print(df)
print('Rows for colum1 value == 4')
print(df.loc[df['col1'] == 4])

Sample Output:

      Original DataFrame
   col1  col2  col3
0     1     4     7
1     4     5     8
2     3     6     9
3     4     7     0
4     5     8     1
Rows for colum1 value == 4
   col1  col2  col3
1     4     5     8
3     4     7     0            

Explanation:

The above code first creates a pandas DataFrame called ‘df’ with three columns 'col1', 'col2', and 'col3' and five rows of data.

df.loc[df['col1'] == 4]: This line selects only the rows of the DataFrame where the 'col1' column is equal to 4. This is done using a boolean mask created by the expression df['col1'] == 4. The .loc method is then used to select only the rows where the boolean mask is True.

Python-Pandas Code Editor:

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